




'''simple docstring'''



from __future__ import annotations

UpperCamelCase_    	=				[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def   lowercase__(			__UpperCamelCase:					list[list[int]]      ,__UpperCamelCase:					list[int]      ,__UpperCamelCase:					list[int]      ,__UpperCamelCase:					int      ,__UpperCamelCase:					list[list[int]]      ,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
          [0 for col in range(len(grid[0]    )    )] for row in range(len(__UpperCamelCase    )    )
      ]  # the reference grid
      SCREAMING_SNAKE_CASE   :      int    					=						1
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
          [0 for col in range(len(grid[0]    )    )] for row in range(len(__UpperCamelCase    )    )
      ]  # the action grid

      SCREAMING_SNAKE_CASE   :      Tuple    					=						init[0]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						init[1]
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						g + heuristic[x][y]  # cost from starting cell to destination cell
      SCREAMING_SNAKE_CASE   :      int    					=						[[f, g, x, y]]

      SCREAMING_SNAKE_CASE   :      List[str]    					=						False  # flag that is set when search is complete
      SCREAMING_SNAKE_CASE   :      Dict    					=						False  # flag set if we can't find expand

      while not found and not resign:
            if len(__UpperCamelCase    ) == 0:
                  raise ValueError('Algorithm is unable to find solution'    )
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						cell.pop()
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						next_cell[2]
                  SCREAMING_SNAKE_CASE   :      Dict    					=						next_cell[3]
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						True
                  else:
                        for i in range(len(__UpperCamelCase    )    ):  # to try out different valid actions
                              SCREAMING_SNAKE_CASE   :      Tuple    					=						x + DIRECTIONS[i][0]
                              SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(__UpperCamelCase    ) and ya >= 0 and ya < len(grid[0]    ):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          SCREAMING_SNAKE_CASE   :      List[Any]    					=						g + cost
                                          SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]    )
                                          SCREAMING_SNAKE_CASE   :      int    					=						1
                                          SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						i
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
      SCREAMING_SNAKE_CASE   :      str    					=						goal[0]
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						goal[1]
      invpath.append([x, y]    )  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						x - DIRECTIONS[action[x][y]][0]
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						y - DIRECTIONS[action[x][y]][1]
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						xa
            SCREAMING_SNAKE_CASE   :      Dict    					=						ya
            invpath.append([x, y]    )

      SCREAMING_SNAKE_CASE   :      int    					=						[]
      for i in range(len(__UpperCamelCase    )    ):
            path.append(invpath[len(__UpperCamelCase    ) - 1 - i]    )
      return path, action


if __name__ == "__main__":
      UpperCamelCase_    	=				[
          [0, 1, 0, 0, 0, 0],
          [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
          [0, 1, 0, 0, 0, 0],
          [0, 1, 0, 0, 1, 0],
          [0, 0, 0, 0, 1, 0],
      ]

      UpperCamelCase_    	=				[0, 0]
      # all coordinates are given in format [y,x]
      UpperCamelCase_    	=				[len(grid) - 1, len(grid[0]) - 1]
      UpperCamelCase_    	=				1

      # the cost map which pushes the path closer to the goal
      UpperCamelCase_    	=				[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
      for i in range(len(grid)):
            for j in range(len(grid[0])):
                  UpperCamelCase_    	=				abs(i - goal[0]) + abs(j - goal[1])
                  if grid[i][j] == 1:
                        # added extra penalty in the heuristic map
                        UpperCamelCase_    	=				9_9

      UpperCamelCase_	,    UpperCamelCase_    	=				search(grid, init, goal, cost, heuristic)

      print("ACTION MAP")
      for i in range(len(action)):
            print(action[i])

      for i in range(len(path)):
            print(path[i])









"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






